学术报告通知：Data-intensive Geospatial Computing

 

      （瑞典耶夫勒大学教授，瑞典皇家工学院兼职教授）

 

 

 

 

    For over one thousand years, human knowledge has been recorded in printed formats such as books, journals, and reports archived and stored in libraries or museums. In the past decades, ever since the invention of computers, human knowledge can also be hidden in digital data. Although part of the data has been researched and reported, the pace of the data explosion has been dramatic and exceeds what the printed medium can deal with. This is particularly true for the 21st century, enormous data volume of scientific data, including geospatial data, collected by all kinds of instruments in a variety of disciplines mostly on a 24/7 basis. The lack of related cyberinfrastructure for the data capture, curation and analysis, and for communication and publication alike led Jim Gray to lay out his vision of the fourth research paradigm - data-intensive science. This fourth paradigm differs fundamentally, in terms of the techniques and technologies involved, from the third paradigm of computational science (c.a. 50 years ago) on simulating complex phenomena or processes. Before the computer age, there was only empirical science (c.a. 1000 years ago), and then theoretical science (c.a. 500 years ago) like Newton’s Laws of Motion and Maxwell’s Equations. Nowadays, scientists do not look through telescopes but instead mine massive data for research and scientific discoveries. This presentation will review the projects carried out by the author and his team in the past years under the framework of data-intensive geospatial computing. We have explored massive geospatial information (at the scale of gigabytes) about city and regional systems yet using the state-of-the-art personal computers for uncovering the underlying structure, patterns, relations and dynamics, or geographic knowledge in general. In the end, I will add a few reflections about scientific research, or geospatial research in particular.